40 research outputs found

    Remote Sensing in Mangroves

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    The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the worl

    Remote sensing for cost-effective blue carbon accounting

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    Blue carbon ecosystems (BCE) include mangrove forests, tidal marshes, and seagrass meadows, all of which are currently under threat, putting their contribution to mitigating climate change at risk. Although certain challenges and trade-offs exist, remote sensing offers a promising avenue for transparent, replicable, and cost-effective accounting of many BCE at unprecedented temporal and spatial scales. The United Nations Framework Convention on Climate Change (UNFCCC) has issued guidelines for developing blue carbon inventories to incorporate into Nationally Determined Contributions (NDCs). Yet, there is little guidance on remote sensing techniques for monitoring, reporting, and verifying blue carbon assets. This review constructs a unified roadmap for applying remote sensing technologies to develop cost-effective carbon inventories for BCE – from local to global scales. We summarise and discuss (1) current standard guidelines for blue carbon inventories; (2) traditional and cutting-edge remote sensing technologies for mapping blue carbon habitats; (3) methods for translating habitat maps into carbon estimates; and (4) a decision tree to assist users in determining the most suitable approach depending on their areas of interest, budget, and required accuracy of blue carbon assessment. We designed this work to support UNFCCC-approved IPCC guidelines with specific recommendations on remote sensing techniques for GHG inventories. Overall, remote sensing technologies are robust and cost-effective tools for monitoring, reporting, and verifying blue carbon assets and projects. Increased appreciation of these techniques can promote a technological shift towards greater policy and industry uptake, enhancing the scalability of blue carbon as a Natural Climate Solution worldwide

    Mapping mangrove forest distribution on Banten, Jakarta, and West Java Ecotone Zone from Sentinel-2-derived indices using cloud computing based Random Forest

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    Mangrove ecosystem is a very potential area, generally located in ecoton areas (a combination of intertidal and supratidal areas), where there is interaction between waters (sea, brackish water, and rivers) with land areas. Indonesia, especially the Banten and West Java regions, have vast mangrove areas and are currently under threat of land conversion. Moreover, mapping the distribution of mangrove forests using the Google Earth Engine platform based on Cloud Computing is less published. Therefore, this research was conducted by introducing the distribution of mangrove forests which involved the Random Forest (RF) classification algorithm method, and looking for the best modification of the index. The combination test was carried out by involving the NDVI, EVI, ARVI, SLAVI, IRECI, RVI, DVI, SAVI, IBI, GNDVI, NDWI, MNDWI, and LSWI indexes. There is a distribution of mangroves in three provinces (West Java, Banten, and Jakarta) which are 933.54 ha (8.372%), 1,537.89 ha (18.231%), and 8,184.82 ha (73.397%). Of the 70 combination tests, the LSWI index (K13, Type-A) is the combination with the lowest accuracy rate of 58.45% (Overal Accuracy) and 39.59 (Kappa statistic), and the combination of K23 (SAVI-MNDWI-IBI) is a combination the best are 96.48% and 92.79. The results and recommendations in this study are expected to be used as a reference in determining policies for the protection of mangrove areas and a reference for further researchEkosistem mangrove merupakan kawasan yang sangat potensial, umumnya berada di kawasan ekoton (kombinasi kawasan intertidal dan supratidal), dimana terdapat interaksi antara perairan (laut, air payau, dan sungai) dengan kawasan daratan. Indonesia khususnya wilayah Banten dan Jawa Barat memiliki kawasan mangrove yang sangat luas dan saat ini terancam alih fungsi lahan. Apalagi pemetaan sebaran hutan bakau menggunakan platform Google Earth Engine berbasis Cloud Computing kurang dipublikasikan. Oleh karena itu, penelitian ini dilakukan dengan memperkenalkan sebaran hutan mangrove yang melibatkan metode algoritma klasifikasi Random Forest (RF), dan mencari modifikasi indeks yang terbaik. Uji kombinasi dilakukan dengan melibatkan indeks NDVI, EVI, ARVI, SLAVI, IRECI, RVI, DVI, SAVI, IBI, GNDVI, NDWI, MNDWI, dan LSWI. Sebaran mangrove terdapat di tiga provinsi (Jawa Barat, Banten, dan DKI Jakarta) yaitu seluas 933,54 ha (8,372%), 1.537,89 ha (18,231%), dan 8.184,82 ha (73,397%). Dari 70 pengujian kombinasi, indeks LSWI (K13, Type-A) merupakan kombinasi dengan tingkat akurasi terendah sebesar 58,45% (Overal Accuracy) dan 39,59 (Kappa statistik), dan kombinasi K23 (SAVI-MNDWI-IBI) merupakan kombinasi yang terbaik yaitu 96,48% dan 92,79. Hasil dan rekomendasi dalam penelitian ini diharapkan dapat digunakan sebagai acuan dalam menentukan kebijakan perlindungan kawasan mangrove dan referensi untuk penelitian selanjutnya

    Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa

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    Creating a national baseline for natural resources, such as mangrove forests, and monitoring them regularly often requires a consistent and robust methodology. With freely available satellite data archives and cloud computing resources, it is now more accessible to conduct such large-scale monitoring and assessment. Yet, few studies examine the reproducibility of such mangrove monitoring frameworks, especially in terms of generating consistent spatial extent. Our objective was to evaluate a combination of image processing approaches to classify mangrove forests along the coast of Senegal and The Gambia. We used freely available global satellite data (Sentinel-2), and cloud computing platform (Google Earth Engine) to run two machine learning algorithms, random forest (RF), and classification and regression trees (CART). We calibrated and validated the algorithms using 800 reference points collected using high-resolution images. We further re-ran 10 iterations for each algorithm, utilizing unique subsets of the initial training data. While all iterations resulted in thematic mangrove maps with over 90% accuracy, the mangrove extent ranges between 827–2807 km2 for Senegal and 245–1271 km2 for The Gambia with one outlier for each country. We further report “Places of Agreement� (PoA) to identify areas where all iterations for both methods agree (506.6 km2 and 129.6 km2 for Senegal and The Gambia, respectively), thus have a high confidence in predicting mangrove extent. While we acknowledge the time- and cost-effectiveness of such methods for the landscape managers, we recommend utilizing them with utmost caution, as well as post-classification on-the-ground checks, especially for decision making

    Integration of Multi-Sensor Data to Estimate Plot-Level Stem Volume Using Machine Learning Algorithms–Case Study of Evergreen Conifer Planted Forests in Japan

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    The development of new methods for estimating precise forest structure parameters is essential for the quantitative evaluation of forest resources. Conventional use of satellite image data, increasing use of terrestrial laser scanning (TLS), and emerging trends in the use of unmanned aerial systems (UASs) highlight the importance of modern technologies in the realm of forest observation. Each technology has different advantages, and this work seeks to incorporate multiple satellite, TLS- and UAS-based remote sensing data sets to improve the ability to estimate forest structure parameters. In this paper, two regression analysis approaches are considered for the estimation: random forest regression (RFR) and support vector regression (SVR). To collect the dependent variable, in situ measurements of individual tree parameters (tree height and diameter at breast height (DBH)) were taken in a Japanese cypress forest using the nondestructive TLS method, which scans the forest to obtain dense and accurate point clouds under the tree canopy. Based on the TLS data, the stem volume was then computed and treated as ground truth information. Topographic and UAS information was then used to calculate various remotely sensed explanatory variables, such as canopy size, canopy cover, and tree height. Canopy cover and canopy shapes were computed via the orthoimages derived from the UAS and watershed segmentation method, respectively. Tree height was computed by combining the digital surface model (DSM) from the UAS and the digital terrain model (DTM) from the TLS data. Topographic variables were computed from the DTM. The backscattering intensity in the satellite imagery was obtained based on L-band (Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2)) and C-band (Sentinel-1) synthetic aperture radar (SAR). All satellite (10–25 m resolution), TLS (3.4 mm resolution) and UAS (2.3–4.6 cm resolution) data were then combined, and RFR and SVR were trained; the resulting predictive powers were then compared. The RFR method yielded fitting R2 up to 0.665 and RMSE up to 66.87 m3/ha (rRMSE = 11.95%) depending on the input variables (best result with canopy height, canopy size, canopy cover, and Sentinel-1 data), and the SVR method showed fitting R2 up to 0.519 and RMSE up to 80.12 m3/ha (rRMSE = 12.67%). The RFR outperformed the SVR method, which could delineate the relationship between the variables for better model accuracy. This work has demonstrated that incorporating various remote sensing data to satellite data, especially adding finer resolution data, can provide good estimates of forest parameters at a plot level (10 by 10 m), potentially allowing advancements in precision forestry

    Operationalization of Remote Sensing Solutions for Sustainable Forest Management

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    The great potential of remote sensing technologies for operational use in sustainable forest management is addressed in this book, which is the reprint of papers published in the Remote Sensing Special Issue “Operationalization of Remote Sensing Solutions for Sustainable Forest Management”. The studies come from three continents and cover multiple remote sensing systems (including terrestrial mobile laser scanning, unmanned aerial vehicles, airborne laser scanning, and satellite data acquisition) and a diversity of data processing algorithms, with a focus on machine learning approaches. The focus of the studies ranges from identification and characterization of individual trees to deriving national- or even continental-level forest attributes and maps. There are studies carefully describing exercises on the case study level, and there are also studies introducing new methodologies for transdisciplinary remote sensing applications. Even though most of the authors look forward to continuing their research, nearly all studies introduced are ready for operational use or have already been implemented in practical forestry

    Structural characterisation of mangrove forests achieved through combining multiple sources of remote sensing data

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    Temporal information on mangrove extent, age, structure and biomass provides an important contribution towards understanding the role of these ecosystems in terms of the services they provide (e.g. in relation to storage of carbon, conservation biodiversity), particularly given the diversity of influences of human activity and natural events and processes. Focusing on the Matang Mangrove Forest Reserve (MMFR) in Perak Province, Peninsular Malaysia, this study aimed to retrieve comprehensive information on the biophysical properties of mangroves from spaceborne optical and Synthetic Aperture Radar (SAR) to support better understanding of their dynamics in a managed setting. For the period 1988 to 2016 (29 years), forest age was estimated on at least an annual basis by combining time-series of Landsat-derived Normalised Difference Moisture Index (NDMI) and Japanese L-band Synthetic Aperture Radar (SAR) data. The NDMI was further used to retrieve canopy cover (%). Interferometric Shuttle Radar Topographic Mission (SRTM) X/C-band (2000), TanDEM-X-band (2010–2016) and stereo WorldView-2 stereo (2016) data were evaluated for their role in estimating canopy height (CH), from which above ground biomass (AGB, Mg ha−1) was derived using pre-established allometry. Whilst both L-band HH and HV data increased with AGB after about 8–10 years of growth, retrieval was compromised by mixed scattering from varying amounts of dead woody debris following clearing and wood material within regenerating forests, thinning of trees at ~15 and 20 years, and saturation of L-band SAR data after approximately 20 years of growth. Reference was made to stereo Phantom-3 DJI stereo imagery to support estimation of canopy cover (CC) and validation of satellite-derived CH. AGB estimates were compared with ground-based measurements. Using relationships with forest age, both CH and AGB were estimated for each date of Landsat or L-band SAR observation and the temporal trends in L-band SAR were shown to effectively track the sequences of clearing and regeneration. From these, four stages of the harvesting cycle were defined. The study provided new information on the biophysical properties and growth dynamics of mangrove forests in the MMFR, inputs for future monitoring activities, and methods for facilitating better characterisation and mapping of mangrove areas worldwide.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

    Quantifying ecosystem services within a reforested urban landscape using remote sensing in eThekwini region of KwaZulu-Natal, South Africa.

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    Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Abstract available in PDF

    Modellering og estimering av tre- og skogressurser i de tørre afromontane skogene i det sør-sentrale Etiopia ved bruk av feltinventering og fjernmåling

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    For effective forest management missions, the presence of accurate information about the forest resources is indispensable. However, the absence of such relevant information for most natural forests in Ethiopia have been challenging the success of management efforts. Lack of appropriate methods, models and data are usually the bottleneck for the availability of the required information. This thesis thus, aimed at identifying and evaluating methods and providing models and data required for estimating tree and forest resources to enhance forest management decision-making. In Paper I, suitability of satellite images for forest area and canopy cover estimation based on visual image interpretation was evaluated. PlanetScope, RapidEye and Sentinel-2 imageries were used under different forest conditions in Ethiopia. Promising results were obtained where the required forest area and canopy cover estimates of large forest areas can be determined with relatively less time than field-based survey. When choosing satellite images, the spatial resolution should be considered carefully. Particularly for densely forested areas finer resolution images should be used. Imageries with high temporal resolution enhance estimation results by offering relatively more cloud free images. In Paper II, models for quantifying basic tree biometric properties including total tree and section volumes were developed and wood basic density data for 30 tree species in Dry Afromontane forests of south-central Ethiopia were documented. The models and data may also be applied to other similar forests. In Paper III, aboveground tree biomass prediction options were assessed for the study sites. The newly developed models are accurate and flexible in application depending on the availability of data from forest inventory. Models developed previously elsewhere were evaluated on our data and generally large prediction errors were observed. Indirect biomass prediction option from volume, using biomass expansion factor and wood basic density, was also assessed and found to be less accurate than the direct method using the newly developed models. Finally, Paper IV assessed the use of satellite images of Landsat 8, Sentinel-2 and PlanetScope for biomass estimation. The biomass model developed in Paper III was used to calculate ground reference values for the 111 systematically distributed sample plots. As a first practical experience for this type of forest in Ethiopia, the possible best spectral and textural variables to be used in biomass prediction models were identified for the respective image types. Model-assisted aboveground biomass estimation method in all the three imageries cases improved estimation efficiency compared to the purely field-based estimation. The estimation efficiency gain from Sentinel-2 was much larger than the others. In general, though further research still required on some gap areas, the developed models along with documented data and evaluated methods in this thesis may have strong practical implications to enhance forest management decision-making including the implementation of REDD+ MRV programs in the Dry Afromontane forests in Ethiopia.WGCFNR ; The Norwegian Governmen
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